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Does gold hedge inflation? On average the answer is no, empirically speaking. But gold’s relationship with inflation is complicated, making any blanket statement about its role in portfolio construction unwise. In this blog post I offer evidence against the claim that gold is a reliable inflation hedge. But I don’t test and thus don’t dismiss gold’s potential value as a diversifier for other reasons. Gold Rush Gold’s recent surge has sent its real (Consumer Price Index-deflated) price to its highest levels since July of 2020 — almost $740 per ounce as of April 2024 — though still below its early 1980 peak of approximately $840 (Exhibit 1). Exhibit 1. This recent high has heightened interest in gold as a portfolio diversifier generally and presumably as an inflation hedge specifically. This blog examines gold’s inflation-hedging properties visually and empirically. Full results and R code can be found in the online R supplement. What an Inflation Hedge Should Do, and What Gold Doesn’t Do An inflation hedge should move with inflation. When inflation goes up, so should the hedge. The claim that gold hedges inflation is therefore testable. To start with, the scatterplot in Exhibit 2 shows the month-over-month change in the headline (that is, “all items”) personal consumption expenditures (PCE) deflator inflation measure versus the spot price of gold from 1979 to 2024, the longest publicly available series for gold prices. Exhibit 2. As evidenced by the random scatter of points in Exhibit 2, changes in headline PCE inflation are not meaningfully correlated with changes in the spot price of gold, on average (correlation coefficient confidence interval = -0.004 to 0.162). And the best-fit line (blue) is flat, statistically. Results are robust to using the Consumer Price Index is used for inflation, though in this case the lower end of the confidence interval is just barely positive—as shown in the online R supplement. The relationship between gold and inflation, however, isn’t stable. There are times when gold’s relationship with inflation is positive, and times when it’s negative. Exhibit 3 shows the rolling 36-month “inflation beta” estimated by regressing the gold spot-price monthly change on the monthly change in headline inflation over a moving 36-month window. Exhibit 3. Sign changes — where the series crosses the dotted horizontal line in the chart above — and large errors indicated by the expansive confidence-interval (two-standard-error) ribbon, which includes zero at just about every point make general statements about the relationship impossible. At the very least, the idea that gold spot price changes move dependably with inflation isn’t supported by this evidence. But there are periods, some protracted, when it does. Casual inspection suggests that the gold-inflation “relationship,” such as it is, is stronger during expansions — the periods between the gray recession bars — except for the Great Recession of 2007 to 2009. Perhaps this is because impulse for inflation matters to its relationship with gold. I look at this possibility next. Decomposing Inflation Using Economic Theory Inflation can be decomposed into temporary and persistent parts, as embodied in Phillips curve models of the inflation process used by economists (Romer 2019). The persistent component is underlying or trend inflation. The temporary part is due to transitory shocks (think oil-price spikes), the impact of which usually fades. What might truly be of interest to practitioners is how gold responds to a rise in underlying inflation resulting, for example, from too much demand or from rising inflation expectations. This kind of inflation can be stubborn and costly (economically) to contain. We can test this response. To do so, we need a measure of underlying inflation. There is a strong theoretical and empirical basis for using an outlier-excluding statistic like the median as a proxy for underlying inflation (see for example Ball et al 2022). The Federal Reserve Bank of Cleveland calculates median PCE and CPI inflation every month, and I use the former measure here, though results are robust to using the latter measure as shown in the online R supplement. A regression of the monthly change in gold on the change in median PCE results in the rejection of any relationship at the usual levels of significance (t -value = 1.61). This is suggested by the shapeless cloud of points in the scatterplot with best fit line (in blue) shown in Exhibit 4. Exhibit 4. Rolling 36-month regressions of gold on median inflation yield results like those for headline inflation. The relationship is unstable and variable (Exhibit 5). Exhibit 5. Interestingly, gold’s median-inflation beta is far more volatile — the standard deviation is about three times larger — and less persistent (as measured by autocorrelation) than headline inflation. That is, gold’s relationship to underlying inflation appears weaker than to headline inflation (regressions confirm this, too — see online R supplement.) One possible explanation is that gold may hedge the difference between headline and median inflation — sometimes called “headline shocks” — more reliably than underlying inflation. That is a point I don’t explore further in this blog post, though I did test the idea briefly in the online R supplement and found no evidence for it.   If underlying inflation captures economic forces of excess demand and rising inflation expectations as embodied in Phillips curve-type models, gold doesn’t appear to hedge the price pressure they can cause. To check the relationship between gold and an overheating economy, I test one more, simple model. Using quarterly real gross domestic product (GDP) and potential GDP estimated by the Congressional Budget Office, I regress gold’s spot-price change on the difference between actual over potential GDP as a measure of economic slack or lack thereof. That is, I regress gold on the GDP “gap.”    A priori, if gold were a hedge against the “demand pull” inflation that can result from an economy speeding up or growing too fast, it should be positively related to the change in the gap. But I find no evidence for this, as shown in the online R supplement. Gold and Inflation: An Unstable Relationship An inflation

What’s more important to success — talent or effort? Most of us would say talent. But according to psychologist and bestselling author Angela Duckworth, most of us would be completely wrong. That’s one of the key takeaways from her bestselling book Grit: The Power of Passion and Perseverance. In an interview with Rosanna Lockwood at the Alpha Summit GLOBAL from CFA Institute, Duckworth, who is also founder and CEO of Character Lab and co-director at the Penn-Wharton Behavior Change for Good Initiative, explained what her research has revealed about the nature of success and how talent and grit contribute to it. From an early age, Duckworth came to understand our tendency to overvalue talent. Her parents’ obsessive focus on success and achievement was a key factor. “It was talked about all the time in our house,” she said. “Who was the most successful person in our family? Who is the most successful of our cousins? Who is the brightest physicist who ever lived? Who is the greatest painter who ever lived?” Her father especially viewed talent as almost synonymous with eventual achievement. But Duckworth took a different tack. “I grew up to become a psychologist who studies pretty much everything that is not your innate talent, your gifts,” she said. “This common denominator that I have identified in high achievers, whether they’re athletes or musicians or investors, is grit.” The basic concept of grit sounds very simple: “passion and perseverance for long-term goals” in Duckworth’s words. But it gets tricky. “This quality of grit is malleable, and it is not correlated at all with measures of talent,” she said. So, not only do we have to overcome the belief that talent defines our potential and limits what we can achieve, but we also have to reframe how we think about making the most of our talents. Consider the so-called 10,000-hour rule. That concept, based on a single study of German musicians, created the misconception that mastery could be achieved simply by putting in the time. But that’s an oversimplification. The actual study found that a very specific and extremely demanding type of practice differentiated the superior from the very good, that the quality of effort over time matters at least as much as the sheer amount of time.  Interpreting these findings through the lens of “grit,” Duckworth broke down the principle into three elements: Concentrate on one specific aspect of overall performance and make deliberate efforts to improve it. Focus on this effort with 100% intensity, with no multitasking, because half-hearted or mindlessly rote “practice” will not suffice. Solicit continuous feedback on how to do better and repeat steps 1 through 3 relentlessly until excellence is achieved. This all might sound like pure persistence. But grit has another critical component: passion. “Happiness and grit and success are all related,” Duckworth said. “Can you become truly world class by doing thousands and thousands of hours of this kind of difficult deliberate practice without loving what you do?” So, there’s nothing magical about the 10,000-hour rule. But there is something magical about hours upon hours spent in high-quality practice. Persistence + Passion = Success? Still, there’s more to the grit equation. Yes, persistence pays off, but most of us still conceive of talent as a rigid, inflexible substance. Duckworth’s research has explored how this mindset influences us, and the hardest part of achieving true grit may be understanding that our abilities are more malleable than we imagine. “Your grit, your curiosity, your humility — there’s nothing about you that is completely fixed when it comes to your mindsets, to your habits, your character,” she said. In Duckworth’s studies, “success” is always defined as objectively possible through countable or measurable criteria. But the malleable nature of our potential is more subjective and depends on conviction. The only way to measure it is to have the passion to persevere in pursuit of something difficult for a long time. Throughout the process, we simply cannot know whether such hyper-focused effort will lead to success. Our preconceptions about the limits of our abilities may constrain us more than our innate abilities. But there is another subjective factor: happiness. Aerodynamic Pursuit “Happiness and success must be related, but they’re not the same thing. Happiness is how you feel about your life. It’s subjective, not objective,” Duckworth said. “Grit not only predicts objective measures of success, but it also predicts subjectively feeling happy, feeling a lot of positive emotion on a daily basis, and also feeling overall satisfied with your life.” So, what will make us happy is grinding persistence in pursuit of achieving some kind of unknown potential that can only be realized after years of sacrifice? As counterintuitive as it may seem, that is exactly what Duckworth’s research suggests. “I think what it really is to be gritty is to have some alignment in your goals, and so you have the opposite of conflict — that you’re aerodynamically pursuing things with a lot of enthusiasm,” she explained. “There’s a wonderful harmony when you feel like what you’re pursuing aligns with your values and aligns with your interests, it aligns with how you’re spending your time. And that’s what I find about very gritty people.” As for achieving happiness, a sense of purpose may be more important than material wealth. “What really motivates people? More than money, honestly, it’s mattering,” Duckworth said. “It’s mattering and being useful and being appreciated by other people.” There’s more. Not only are gritty people happier, but they also tend to score high on other virtues. “There is a positive correlation between grit and kindness, gratitude, empathy, curiosity, and more,” she said. “These things are positively correlated, but they’re not exactly the same. So, we should be reminded of the importance of ethics and other people.” Beyond Individual Achievement Grit is not developed in isolation but in a context. And culture is a critical element of that process, according to Duckworth. Shared beliefs, values, and rituals at the national, local, and

“There are these people who think we don’t have to take all these tough decisions to deal with our debt. . . . It’s as if they think there’s some magic money tree. Well, let me tell you a plain truth: there isn’t.” — David Cameron, UK Prime Minister, 2010 to 2016 How does public debt influence an economy’s long-term potential? A decade ago, some economists claimed public debt in excess of 90% of GDP led to negative growth. Others disputed these parameters but conceded that advanced economies with public debt above 90% of GDP averaged 2.2% annual growth between 1945 and 2009 compared to 4.2% for those with a ratio below 30%. Whatever the relationship between sovereign debt and economic growth, many developed economies have debt burdens well in excess of that 90% threshold. When its then-prime minister David Cameron emphasized that more deficit spending was out of the question, the United Kingdom had a debt-to-GDP ratio below 80%. After a decade nurturing the alchemistic money tree, that figure is now 100%. In the United States, after 40 years of almost uninterrupted supply-side “trickle-down economics,” this ratio is over 120%. Should governments ever decide to end this permissive environment and start deleveraging, how could they do it? 1. Redeem Governments can discharge public debt by selling off infrastructure and other state property. Following the eurozone crisis of the 2010s, for example, Greece sold several of its air- and seaports and a large stake in its telecoms operator OTE, among other assets, to erase part of its liabilities. States can also requisition the assets of their citizens and corporations. In the 16th century, Henry VIII dissolved monasteries in England and disposed of their property to fund his military campaigns. During the French Revolution, the Constituent Assembly confiscated the clergy’s estates and auctioned them off to wipe out the public debt. Taxation rather than outright expropriation is a much more common appropriation technique, however, whether through higher marginal income and capital tax rates, as the Joseph Biden administration proposed, or through an exceptional tax. In the United States, some economists and politicians support a wealth tax to address economic inequality and generate extra revenue to pay down the debt. In the United Kingdom and other nations that have yet to overhaul their property laws, taxing land value is a viable alternative. Of course, with globalization and sweeping financialization, tax evasion and avoidance schemes have grown ever more sophisticated. Without international cooperation, wealth tax collection can be neither easy nor fair. 2. Prune A more effective debt amortization strategy is to let prices rise. Amid increased output and government revenues, inflation mechanically lowers the debt-to-GDP ratio as the denominator expands. In the aftermath of the 1970s oil shocks, for example, US public debt fell from 35% to 30% as a percentage of GDP. Not only does the principal fall in value, if interest charges remain below the price index, as they have in many developed countries over the last 18 months, negative real interest rates reduce the debt service burden. With inflation at or close to double digits, interest rates in the low single digits make interest repayments much more manageable. Naturally, bonds linked to the retail price index, which represent about 25% of UK public debt, provide no such comfort. The US Treasury first issued government-guaranteed inflation-indexed bonds in 1997 — when many thought inflation was permanently tamed — but paid close to double digit interest rates on them last year. If maintaining zero or negative interest rates on a real-term basis is a standard technique of financial repression, the current situation demonstrates that controlling price increases is challenging, while the 1970s scenario shows that reducing sovereign debt via inflation takes time. Either way, such arrangements are harmful to savers and consumers alike. Currency devaluation can also lower debt-servicing costs. It has been unofficially endorsed by the United Kingdom since exiting the European Union. Through such depreciation, countries that issue public debt in their own currency facilitate the redemption of that debt since government bonds’ interest payments are primarily fixed. Budget deficit reduction is even more effective. Government spending cuts combined with increased revenues eventually produce budget surpluses. This is what Cameron’s government sought to accomplish during the Great Recession. But success is far from assured. Such efforts require phasing out popular programs and sustained fiscal discipline and can take decades to bear fruit. The United States has only recorded four years of surplus in the last 50. France last reported a balanced budget half a century ago. A less painful way to shrink the public debt is for borrowers — whether individuals, corporations, or nations — to grow into their debt structure. But stimulating growth is not a straightforward exercise. Over the last 30 years, Japan has increased its debt-to-GDP from 40% in the early 1990s to 220% or more today without generating the hoped-for economic expansion. Growing out of debt is hard and when central banks maintain tight monetary policies amid inflation fears, it is pretty much impossible. 3. Amend Restructuring may be a more credible way to manage sovereign debt. “Independent” central banks purchased government bonds to keep the economy afloat throughout the 2010s and resorted to even more unconventional monetary policies during the pandemic. Since the global financial crisis (GFC), the US Federal Reserve’s balance sheet has expanded by a factor of 8 while the Bank of Japan’s multiplied sevenfold. This debt-vacuuming strategy lowered interest rates to zero and the cost of debt evaporated. Rather than flood public markets with sovereign bonds, governments chose to temporarily park them off market. But the post-pandemic contraction is making it difficult for central banks to offload these bonds. Creditors could also voluntarily waive their redemption rights. The so-called debt jubilee was common in ancient times, but such debt forgiveness has not occurred in Europe since the aftermath of World War II. Since central banks have become their countries’ major creditors, this option may be more feasible today. While the

The following is based on “Redefining the Optimal Retirement Income Strategy,” from the Financial Analysts Journal. Last month, I explored how retirees typically have some ability to adapt their spending to prolong the life of their portfolio. Here, I introduce an approach that incorporates dynamic spending into retirement income projections and provide an example of how it can result in more realistic expectations of potential retirement spending paths. Evolving Models Retirement income planning tools largely assume “static” spending: That is, portfolio withdrawals are expected to change over time based on inflation or some other constant factor. This assumption is overly simplistic and inconsistent with the decisions retirees might make when faced with potential portfolio ruin. In reality, retirees cut or increase their spending based on how their situation develops. If their portfolio performance falls below expectations, for example, they may need to tighten their belts, and vice versa. While research going back decades proposes various methods to adjust portfolio withdrawals over time, these so-called dynamic spending (or withdrawal) rules can be difficult to implement. They may be too computationally complex or otherwise unable to handle nonconstant cash flows, and they may significantly complicate financial planning tools and even “break” more common binary outcome metrics, such as the probability of success. Static spending rules lead to retirement income projections that can differ significantly from the likely choices a household would make in retirement and from the optimal decisions around how that retirement should be funded. Introducing the Funded Ratio The funded ratio metric measures the health of pension plans, but it can also estimate the overall financial situation of retiree consumption or any other goal. The funded ratio is the total value of the assets, which includes both current balances and future expected income, divided by the liability, or all current and future expected spending. A funded ratio of 1.0 implies that an individual has just enough assets to fully fund the goal. A funded ratio greater than 1.0 suggests they have a surplus, while one below 1.0 implies a shortfall. Estimating the funded ratio for each assumed year using a Monte Carlo simulation is one way to adjust expected spending throughout retirement as the retiree’s situation evolves (e.g., based on market returns). The table below provides context around how a certain spending amount could be tweaked based on the funded ratio for the respective goal at the end of the previous year. Real Spending Adjustment Thresholds by Funding Ratio Level Funded Ratio Needs Goal Wants Goal 0.00 -10% -20% 0.25 -5% -15% 0.50 -3% -10% 0.75 0% -5% 1.00 0% 0% 1.25 0% 2% 1.50 0% 4% 1.75 2% 8% 2.00 4% 10% For illustrative purposes only. Based on the above, if the wants spending goal is $50,000 and the funded ratio was 1.40, the amount would increase by 2%, to $51,000, in the subsequent year. Anticipated spending falls as the funded ratio declines, and vice versa. The changes to the needs and wants spending adjustments vary, with greater adjustments to the latter. These differences reflect how much assumed flexibility is embedded in the two spending goals and the diminishing marginal utility of consumption. We could significantly increase the complexity of the adjustment rules, for example, by considering the remaining duration of retirement, portfolio risk levels, or additional client preferences. While this dynamic spending model resembles some existing approaches, it is more holistic in how it considers the retiree’s situation. Other common dynamic spending rules, such as variants of how required minimum distributions (RMDs) are determined from qualified accounts, focus entirely on the portfolio balance and cannot incorporate how the role of the portfolio funding retirement could vary over time. Most dynamic spending rules cannot model a scenario in which spouses retire and claim Social Security at different ages and receive future sources of guaranteed income, such as a longevity annuity starting at age 85. The Impact on Income Incorporating dynamic spending rules can reveal a very different perspective on the range of potential retirement outcomes than viewing retirement as a static goal. For example, the exhibit below shows how spending could evolve for a retiree with an $80,000 retirement income goal, $1 million in savings, and $40,000 in Social Security benefits for whom 70%, or $56,000, of the total $80,000 goal is classified as needs. Distribution of Simulation Outcomes While the probability of success for this simulation is approximately 70% assuming a static retirement income goal based on the key modeling assumptions in the research, overall the retiree does relatively well. The likelihood of missing their retirement income goal, especially the amount they need, is incredibly low. Conclusion While financial advisers often say they are dynamically adjusting client spending throughout retirement based on how the retiree’s situation develops, the related decisions are not generally incorporated into the actual plan when it is based on static assumptions. This creates a significant mismatch. Integrating dynamic rules into a retirement income plan can have significant implications on optimal retirement income decisions and must be included in financial planning tools to ensure the modeled outcomes and potential guidance better reflect the realities of retirement. For more from David Blanchett, PhD, CFA, CPA, don’t miss “Redefining the Optimal Retirement Income Strategy,” from the Financial Analysts Journal. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / jacoblund Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Investment leaders operate in a high-stakes world where every decision carries weight. Yet, one of the biggest risks isn’t found in market data or economic forecasts — it’s in their own judgment. The tendency to confuse luck with skill can lead to overconfidence in bull markets and misplaced blame in downturns. Leadership in investing requires the ability to separate process from outcome, ensuring that decisions are evaluated on their merit, not just their results. This is the final post in my series about leadership-focused self-improvement. I’ll be speaking about these topics during a panel discussion at CFA Institute LIVE 2025. This is a quick read reminding us about the hidden trap sabotaging our decisions: our egos. Our egos are hardwired to fall into the trap of confounding luck and skill.  Suppose you decide to drive drunk and you make it home safely. That was a bad decision with a good outcome.  One week later, after a good night of drinking Zinfandel, you ask a designated driver to drive you home. The driver gets into an accident. That was a good decision with a bad outcome. (Setting aside that you drank Zinfandel, which clearly is a horrible decision.) Because of randomness, outcomes are often silent on the quality of decisions. Worse, they can mislead. In a world in which we can’t predict much of the future, good decisions can lead to bad outcomes, and bad decisions can lead to good outcomes. In the business of investment management, we say there’s “randomness.” To manage this, investment leaders must be clinical about their wins and losses. Confusing Luck and Skill in the Investment World This problem is acute in the investment world. You can make money, at least for a while, by making bad decisions like holding a concentrated portfolio or investing in fads. If you don’t examine your process and the quality of your decisions, in other words, if you only focus on outcomes, you may think you’re an absolute genius. But you’re unlikely to be a successful investor in the long run. Annie Duke’s excellent book, Thinking in Bets, has become required reading in the investment world. Duke is a business consultant and ex-professional poker player. She explains that we instinctively associate good results with good decisions and bad results with bad decisions. She calls this instinct “resulting.” But in poker and many aspects of life, “winning and losing are only loose signals of decision quality,” she says. Differentiating Between the Two To help differentiate between the two, cultivate self-awareness. Focus on your decision-making process rather than outcomes. When you’re winning, remember that luck may be involved. This is hard. We all have this reflex of wanting to take credit for our wins.  And if you miss your target, don’t beat yourself up. Is it possible you made the right decisions but got unlucky? That’s easier to tell yourself.  Quoting one of my mentors:  “There are only two types of investors: those who are talented and those who are unlucky.” Key Takeaway Great investment leadership isn’t about being right all the time — it’s about fostering a process that prioritizes sound decision-making over short-term outcomes. By recognizing the role of chance and reinforcing analytical discipline, investment leaders can build more resilient strategies and teams. In an unpredictable financial world, the best leaders don’t just chase returns, they cultivate the judgment and processes that drive sustainable success. Sébastien Page, CFA, is the author of The Psychology of Leadership. Career-Related Content Blinded by Success: How Obsessive Goal-Setting Can Backfire in Finance and Beyond For Investment Leaders: Why You Should Learn to Love Losing For the Investment Professional: The Mindset Shift that Changes Everything Women and Finance: How Embracing Risk Can Unlock Greater Success 2025 Wealth Management Outlook: Spotlight on Investment Careers Climbing the Ladder in Finance: The PIE Framework for Investment Professionals source

The rapid ascent of the federal funds rate from near 0% in 2022 to a 15-year high of 5.25% in July 2023 presents both an opportunity for hedge funds’ expected returns and a silent increase in the price of alpha. Indeed, given the interest rate trajectory, the alpha captured by those who invested with a good manager with an equity beta of 1 may have fallen by 36%. So, how can hedge fund investors optimize the price they pay for alpha? The Alpha Capture Ratio The alpha capture ratio metric gauges the cost of alpha. To calculate it, we first apply the capital asset pricing model (CAPM) to measure the net alpha return for managers with varying equity betas in different interest rate environments under a given equity risk premium. Net Alpha = Net Returns – Risk-Free Rate – (Equity Risk Premium * Equity Beta) Since managers should not charge active rates for beta, we treat all management fees as the cost of generating alpha and define gross alpha as follows: Gross Alpha = Net Alpha + Management Fees + Performance Fees With the resulting alpha capture ratio, we can compare managers with different equity betas in different interest rate environments. Alpha Capture = Net Alpha / Gross Alpha How Do Different Manager Risk Profiles Impact the Alpha Capture Ratio? To answer this question, we created two hypothetical managers: a Good Manager and a Bad Manager who achieve a gross alpha of 7% and 3%, respectively. Assuming a 2 and 20 fee structure of 2% management and 20% performance fees with no risk-free rate performance fee hurdle, how would their performance compare in an environment with a 6% equity risk premium? When the risk-free rate is 0%, investors retain 40% to 54% of the Good Manager’s alpha across equity beta levels of 0.2, 0.5, and 1. As the risk-free rate rises to 5%, however, the rate of alpha capture declines by between 27% and 36%, indicating a substantial spike in the price of alpha. This leads to two observations: First, the rate of alpha capture diminishes the higher the equity beta levels because the returns generated by equity beta drive up the absolute performance fee charged by the fund and consequently reduce net alpha. Second, the rise in the risk-free rate has a more pronounced negative effect on the price of alpha for managers with higher equity beta levels. Alpha Capture: Good Manager with 2 and 20 Fee Structure In the case of our Bad Manager with an equity beta of 0.2, when the gross alpha drops from 7% to 3%, alpha capture falls from 54% to 19%. This downward trend in the alpha capture rate persists as the equity beta increases. Such a steep decline reflects the importance of manager selection. Alpha Capture: Bad Manager with 2 and 20 Fee Structure In both scenarios, as the risk-free rate rises, so does the price of alpha, assuming the expected return of alpha and the equity risk premium remain unchanged. Alpha Capture with Different Fee Structures and Risk-Free Rates Alpha capture rates vary depending on the fee structure and the risk-free rate. To illustrate this phenomenon, we compare the performance of three different pricing structures: one with a 1% management and 20% performance fee, another with a 2% management and 10% performance fee, and a third with a 2% management and 20% performance fee as well as a performance fee hurdle. Under the lower fee structures — our 1 and 20 and 2 and 10 scenarios — the alpha capture rate rises. But the rate of alpha capture declines roughly twice as much — between 22% and 28% — when the management fee drops from 2% to 1% than when the performance fee is lowered to 10% from 20%. In the latter scenario, the alpha capture rate falls by between and 11% and 13%. This discrepancy underscores the influence of performance fees on alpha capture rates amid a higher risk-free rate. Alpha Capture: Good Manager with 1 and 20 Fee Structure Alpha Capture: Good Manager with 2 and 10 Fee Structure Given the influence of rising interest rates and performance fees on alpha capture, investors should engage with managers to implement a risk-free rate performance fee hurdle. The charts below explore the rate of alpha capture under the different fee structures during both a 0% and 5% risk-free rate environment and compare the base case 2 and 20 fee structure with three alternatives: one with a 1% management fee reduction, a second with a 10% performance fee reduction, and another with a risk-free rate performance fee hurdle that assumes the investor has a positive conviction about the manager. These scenarios raise two important points. First, there is no ideal fee structure across the scenarios. With a low 0.2 beta manager in a 0% risk-free rate environment, the 1 and 20 fee structure would be optimal for an investor, delivering the highest alpha retention of 65%. But if the risk-free rate climbs to 5%, a lower performance fee structure — our 2 and 10 scenario — would work better. Conversely, with higher beta managers — 0.5 and 1 beta — the 2 and 10 structure would also be preferable. Second, if investors cannot negotiate management or performance fee discounts, a risk-free rate performance fee hurdle could be an acceptable compromise. When the risk-free rate increases to 5%, the alpha capture rate falls somewhere between the rate observed with lower management fees and that with lower performance fees. Looking Ahead In the current high interest rate environment, investors should try to maximize alpha capture by negotiating a discount on performance fees rather than management fees. Failing that, they should try to implement a risk-free rate performance fee hurdle. All told, investors should consider the impact of a performance fee hurdle when inferring a manager’s future performance. In the past, since the risk-free rate was practically zero, there was little to no track record distortion due to the potential performance fee hurdle. With

Read the complete three-part series. The European Central Bank (ECB) began buying corporate debt as part of its corporate sector purchase programme (CSPP) in 2016. Given its clear and unambiguous legality, the CSPP could create the expectation among investors of an “ECB put,” that the bank will do “whatever it takes” to provide liquidity and restore order to the financial markets in the event of a crisis. Thus, the program may have a lasting effect on European credit markets. So, what does the data show? Has the CSPP repriced corporate credit in Europe? Unlike the Fed, the ECB has confined its corporate sector bond purchases, detailed in the chart below, to investment-grade (IG) debt. While the Fed bought $14 billion in bonds and exchange-traded funds (ETFs) in 2020, the ECB purchased as much in the first two-and-a-half months of the COVID-19 pandemic alone. These purchases accounted for a much higher percentage of the European corporate bond market, which is less than half the size of its US counterpart. ECB Corporate Bond Purchases: Significant and Persistent As of 31 August 2022Source: Bloomberg But if the US experience is any guide, investor perception is influenced not just by the scale of the bond buying but also by how confident market participants are that the central bank will intervene in difficult times. Option-Adjusted Spreads The historical option-adjusted spreads (OAS) for European A-rated and BBB-rated corporate debt, visualized below, widened to all-time highs during the global financial crisis (GFC) and widened again during the European sovereign debt crisis in 2011. While the ECB launched support programs to counter the GFC and expanded them to the banking sector amid the sovereign debt crisis, it did not directly buy assets until 2016. Euro Corporate Option-Adjusted Spreads (OAS) As of 31 December 2021Source: ICE data Since then, the 2020 sell-off has been the Fed’s and ECB’s most significant challenge and the first instance where evidence of a central bank put might surface. Like the Fed, the ECB stepped up its asset purchases in response, and credit spreads returned to their pre-COVID-19 levels by year-end 2020. While crises precipitated by different catalysts don’t make for apples-to-apples comparisons, spreads increased much less during the pandemic than in the two previous sell-offs. Perhaps the ECB and other centrals learned from past experience and took swifter action. A look at the history of credit spreads before and after the inception of ECB asset purchases shows no conclusive evidence of an “ECB put.” But it does suggest that the market has changed since the ECB first intervened. Median credit spreads for A-rated debt in Europe are in line with pre-CSPP levels, according to the preceding illustration, while spreads for lower-rated BBB debt have narrowed since 2016. Of course, in a lower interest rate environment like that of the last several years, investors’ hunger for yield grows. US credit spreads tell the same story. If there is an expectation that central banks will intervene during crises, greater risk-taking looks “safer.” Yet the lower median for spreads also occurred amid a massive increase in corporate credit issuance and in corporate leverage. Pandemic Induced Spread Widening More Muted Than Past Crises 1. 30 June 2007―18 December 20082. 4 May 2011―29 November 20113. 21 February 2020―3 April 2020Source: ICE data Annualized spread volatility, calculated from weekly changes in spreads, is displayed in the graphic that follows. Since the start of the CSPP, spread volatility has decreased. While correlation is not causation, lower spread volatility and lower equilibrium spread levels could signal an implicit ECB put. Though the Fed was also buying debt during this period, its purchases were limited to government bonds and agency mortgage-backed securities (MBS) until the pandemic broke out. While median spread levels have declined in the United States, economic growth was relatively robust as investors pursued riskier assets. The substantial increase in downgrades could explain why BBB spreads exhibited somewhat higher volatility as corporations took advantage of lower yields to lever up their balance sheets. Credit Spreads Better Behaved in the Presence of ECB Buying? As of 31 December 2021Sources: : ICE data and MacKay Shields The corollary to this situation is a central bank put would be expected to mitigate extreme spread widening and lead to lower volatility. The distribution of spreads would then have shorter tails, or at least shorter right tails. The following exhibit bears this out. Thinner Tails Exhibited but Dearth of Events Gives Pause 1. January 1998―December 20152. January 2016―December 2021Sources: ICE data and MacKay Shields Because of the inherent asymmetry of corporate debt, we would anticipate the distribution of weekly spread changes before and after the inception of the CSPP to have a fatter right tail. While there is a fat tail, it is not as pronounced in A-rated debt — though it surely would be in below-IG debt and other markets with higher default risk. The distribution for BBB-rated corporate debt is similar in shape and, given the lower spread volatility, the tails are shorter. Again, the post-CSPP period is only six years old with fewer extreme events than the 18 years prior. Nevertheless, the modestly tighter spreads, lower spread volatility, and shorter right tail could indicate a central bank put. Realized Spread Behavior vs. Fair Value Model We also looked for evidence of an ECB put by comparing realized spread behavior with several fair value models of corporate spreads published by major investment banks. These models are based on monthly estimates of the fair value of broad-based market spreads and apply a set of sensible factors to estimate the price of credit risk. The UBS model, which we focus on here, uses explanatory variables that capture economic fundamentals, credit performance, and market liquidity measures to estimate the fair level of spreads. Modeled spreads have historically tracked realized spread behavior, as shown in the following illustration. Spreads Widen by Less Than Expected in 2020 The pandemic sell-off in March 2020 is the first extreme market event since the CSPP’s inception, and according to the

Introduction We have analyzed dozens of public and private market investment strategies, such as merger arbitrage and private equity, respectively, over the last few years, and one common theme has emerged. Most of the products described in more than 300 research papers simply provide exposure to the stock market in complicated wrappers. Once the tide goes out, the risk exposure is the same everywhere. We can demonstrate this phenomenon in different ways. The most common approach is to simply run a factor exposure analysis. Investment products marketed as offering uncorrelated returns often exhibit high betas to the stock market, which highlights a lack of diversification benefits. But there is an even simpler and perhaps more powerful way to illustrate this point: by using a combination of the S&P 500 and cash to replicate the historical performance of an investment product with the same level of risk. We recently created Time Machine, a freely accessible tool with which investors can replicate the performance of any mutual fund, exchange-traded fund (ETF), or US stock using just the S&P 500 and cash. To demonstrate Time Machine’s facility on social media, we analyzed the iMGP DBi Hedge Strategy ETF (DBEH), which tracks the 40 leading long–short equity hedge funds, and found that an 81% allocation to the S&P 500 and a 19% allocation to cash would have delivered almost the same performance with the same volatility. Replicating a Long–Short Hedge Fund ETF with the S&P 500 and Cash Source: Finominal From our perspective, these Time Machine results called the utility of this ETF into question. A respected Twitter commentator, on the other hand, countered that the fund’s three-year track record was too short to draw any conclusions and that our replication process was simply based on hindsight. These were fair points, so we expanded our analysis. Long–Short Equity Hedge Fund Performance Since the goal is to replicate equity-like returns with less risk, or exactly what a S&P 500 plus cash portfolio provides, we use long–short equity hedge funds as case studies. To evaluate each, we selected indexes that have extended histories across multiple market cycles. The Eurekahedge Long Short Equities Hedge Fund Index and HFRX Equity Hedge Index both have 20 years of history, which should be sufficient. But Eurekahedge has a CAGR of 8.1% versus 2.0% for HFRX. Given that both aggregate the returns of single long–short equity hedge funds, such a large discrepancy is alarming and makes it difficult to evaluate each strategy’s attractiveness. Which one is better? Of course, the number of funds in each index varies, but the crucial driver may be that Eurekahedge allows new fund managers to import their past track records once they start reporting. Since only fund managers with good past performance ask to be included in these indexes, a form of survivorship bias may be at work. So, capital allocators would be wise to ignore the Eurekahedge index and focus, as we do in the rest of our analysis, on the more realistic HFRX. Long-Term Performance of Long–Short Equity Hedge Funds Source: Finominal Replicating Long–Short Hedge Funds The HFRX Equity Hedge Index’s volatility was 6.1% over the 2003 to 2023 period, which we could have replicated with a 52% allocation to the S&P 500 and 49% to cash. But the replication portfolio’s CAGR would have been 3.7% compared with 2.0% for the hedge funds, and the drawdown would have fallen from 31% to 19%. This results in significantly higher risk-adjusted returns for the replication portfolio. To be sure, investors do not have to conduct any due diligence on the S&P 500, whereas hedge fund analysis is an expensive process that requires an initial assessment as well as ongoing monitoring. Furthermore, an S&P 500 ETF today has basically zero expenses, while hedge funds come with high management and performance fees. So, who wouldn’t favor the replication portfolio? Replicating HFRX Equity Hedge Index with S&P 500 and Cash Source: Finominal Further Thoughts Although a simple S&P 500 and cash portfolio would have achieved higher absolute and risk-adjusted returns than long–short equity hedge funds, might our analysis still be based on hindsight and have little relevance for expected returns? Yes, but given the 0.71 correlation between the HFRX Equity Hedge Index and the S&P 500, there is little question that long–short equity hedge funds offer diluted equity exposure. Furthermore, the HFRX index’s upside beta to the S&P 500 was 0.16 compared with 0.25 on the downside. As such, equity hedge funds follow falling stocks more than rising ones. Obviously, this ratio is at parity for any S&P 500 and cash combination. At some point, hindsight becomes foresight. For more insights from Nicolas Rabener and the Finominal team, sign up for their research reports. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / Ryan Djakovic Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

The proxy voting system has taken on added importance amid growing interest in environmental, social, and governance (ESG) issues and how investors vote on related shareholder proposals. Institutional Shareholder Services (ISS) and Glass Lewis, in particular, provide critical guidance, and shareholders who want to vote their proxies depend on them to execute their proxy votes in a thoughtful and transparent manner. But with only two players dominating the space, are investors really getting the diversity of perspectives they need to make informed proxy votes? That’s where a new database, OxProx, comes in. Founded by Ian Robertson, CFA, who co-authored this article, OxProx is a social venture spin-off with the University of Oxford and is the first publicly accessible service that gathers, examines, and compares proxy voting records among asset owners and asset managers from across the globe. The Importance of ISS and Glass Lewis To be sure, by offering research, guidance, and record keeping, ISS and Glass Lewis are essential pillars of the proxy voting system. Investors with thousands of stocks in their portfolio can hardly research each proxy vote. They are better off instituting a policy to guide their voting while paying additional attention to specific issues that align with their expertise and interests. Are Two Platforms Enough? Regulators, academics, and investors have acknowledged that the preeminence of ISS and Glass Lewis, may lead to a dearth of alternative views on how shareholders should vote, and that may compel investors to default to the recommendation of the proxy advisory service they subscribe to. Pension plans and other large investors have the capacity to mitigate this duopoly issue by subscribing to either or both ISS and Glass Lewis and incorporating their guidance as an information input rather than a default vote. They can then layer in their own research and perspectives on certain proxy votes over and above what ISS and Glass Lewis provide or what would be possible through a standard voting policy approach. Due to time and cost constraints, however, many small- and medium-sized firms have no choice in most instances but to defer to the two dominant players. A Helpful Step With OxProx‘s publicly accessible and searchable database of global proxy voting records, investors and analysts can compare how investors voted their proxies and what their voting rationales were if and when they are disclosed. When does an investor’s proxy vote align with ISS and Glass Lewis guidance? When does it differ? Does an investor always support management or shareholder proposals? OxProx makes such data available and findable. Vote disclosure requirements and practices are governed at the national level, so as a global database, OxProx facilitates more robust comparisons. Who Benefits from Proxy Data Transparency? ShareAction in the United Kingdom, As You Sow in the United States, and SHARE in Canada, among other stakeholder and shareholder advocacy groups, are all doing important work on ESG issues. But these organizations do not view proxy voting through a shareholder or financial materiality lens. That is, they are not overly influenced by whether shareholder returns will be materially affected by certain ESG decisions. Rather, they engage with companies and industries on greenhouse gas emissions and other ESG issues to advocate for changes that will benefit all stakeholders and society at large even if they may reduce shareholder earnings. OxProx data can help inform these organization on how to approach and hold firms and investors accountable when their policy voting runs counter to both long-term shareholder and stakeholder interests. The contested director elections at Exxon Mobil in 2021 is a case in point. Skeptical of the company’s carbon-transition strategy, investment firm Engine No. 1 led a successful activist campaign for seats on the board. The Investor and the Adviser The proxy voting system is a crucial conduit for public companies and their investors, with ISS and Glass Lewis leading the way. But the transparency and accountability challenges are real and OxProx can help address these deficits by providing accurate and timely data on how different investors have voted. As ESG factors become increasingly integral to investment decisions, platforms like OxProx can help promote responsible investment and drive positive change in corporate outcomes. For many years reconciling ESG issues with investment performance posed a challenge to fiduciaries who equated ESG considerations with screening stocks from portfolios. All things being equal, a screened portfolio is less diversified. Absent market mispricing of the screened investments, such a portfolio will yield lower risk-adjusted returns. Incorporating material ESG issues into investment analysis and security selection is now standard practice for active managers and considered part of their fiduciary duty. For some managers, engaging with select companies on ESG issues can provide extra analytical insight and encourage investee companies to pursue better shareholder and stakeholder outcomes. Stakeholders and advocacy groups may in turn nudge investment managers to seek better ESG outcomes. While these may not increase financial returns, they may not detract from them either. Indeed, the transparency OxProx provides may persuade investors to improve their proxy voting on ESG issues — to the point where there are no diminished financial returns and ESG proposals with positive net present value (NPV) have greater support. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author(s). As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / deeaf Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

The Little Book of Picking Top Stocks: How to Spot the Hidden Gems. 2023. Martin S. Fridson, CFA. John Wiley & Sons, Inc. Editor’s note: In order to keep our book review selection process impartial and free from bias, Martin Fridson, CFA, was not involved in the decision to review the book or the writing and editing of this review. When I first saw the title of Martin Fridson, CFA’s latest masterwork, I wondered what the focus could possibly be other than hitting paydirt or selecting the winning horse, which happened to be a long shot. Considering Fridson’s deep background in fixed-income analysis, I initially thought a secret edge could be found by using intense credit analysis or tracking the rise and fall of a company’s credit ratings. But what happens when a company does not have credit ratings — or has very low ones? This “little” book with big ideas presents a novel approach that to date has not been systematized in such an evidence-based style as presented here. Do you want to get hooked into identifying the best performing stock? One may consider this instant gratification, and it certainly is! Yet, there is a clear method to it that lies outside the world of the Wall Street analysts who are essentially spoon-fed the same information by corporations — especially when it relates to forecasting EPS for a quarter or a year — and then who set a price target and make a Buy or Sell call. The author states that the bulk of stock ratings falls into the Buy/Hold category, with a Sell recommendation rarely seen. Is there really such a rating as Hold, which could be a “wink-wink” Sell? Analysts deserve recognition for what they do best: the fundamental analysis of a group of companies in an industry and tracking their fortunes. But can such analysis be relied on to hit the home run of a best performing stock? Fridson details the fundamental and industry-specific stories of the top S&P 500 Index stocks in each of the years 2017 through 2021. He also delves into the importance identifying free cash flow and estimating its trend in contrast with net income, or EPS, and even GAAP (generally accepted accounting principles) earnings. Another suspect item to consider is “earnings management,” which many corporations use to “smooth” reported earnings. Each stock’s unique and detailed analysis is presented, with the “worst case” achieving an 80% return in 2018, when the S&P 500 stocks delivered a return of –6.24%. Readers will recognize each of the names but may be startled to learn the catalysts for performance that Fridson identifies. The items that stood out to me more than others are an uptick in free cash flow generation, improving credit (generally from bad to less bad), restructuring, the choice of special dividends versus consistently raising dividends, and unique market circumstances. Identifying the winners of the past and understanding the pulse points for exceptional price performance provide clues as to what follows later in the book.      Keep in mind the non-S&P 500 stocks that delivered eye-popping performance for the same period. Fridson details their circumstances for the years 2017–2019. The catalysts are similar to the names of the bigger stocks. Here, though, one is dealing with smaller (but not necessarily so) capitalizations, a lack of sequential positive earnings, and perhaps fewer publicly traded shares. If one reviews the records of top stocks for the years that are not included in The Little Book of Picking Top Stocks, 2020 and 2021, one will find unusual catalysts that could not have been identified before their time in the sun. In 2020, Nio Inc. (NIO) gained 1,103%, making it the only large-cap issue in the top 10 non-S&P 500 stocks that year. And in 2021, the top stock was GameStop (GME), rising 815%. The book crescendos to its detailed quantitative and qualitative presentation in its back half. The quantitative characteristics presented are strikingly evidence based and give readers a green light of sorts to initiate their own analysis. These are based on stock price volatility (the higher the better), dispersion in EPS forecasts (the greater the better), bond ratings, and market capitalization. The reader may be surprised to find “EPS dispersion” on the list given that EPS typically runs quite tight in Wall Street research, as discussed at length. Fridson and researcher John Lee have devised a strikingly simple statistic, the Fridson–Lee statistic. Markedly greater EPS estimate dispersion is observed in the top stock as compared with the “average” S&P 500 stock (i.e., the 250th stock). Readers will also enjoy the “blown plausible hypotheses” that are discussed and the explanations for why they do not work. The qualitative characteristics Fridson addresses focus on outside pressure for change, dynamic technology, signs of potential credit improvement, and competitive dominance. Do I hear the name Tesla? Readers will remember the 2020 narrative fondly — even though that particular year began with more Sell ratings than Buy ratings on the stock. Fridson’s The Little Book of Picking Top Stocks will encourage analysts and investors to do something they may be unfamiliar with: going for No. 1 systematically. The goal need not be attaining the very pinnacle of stock price performance in a single year, but investors could come satisfyingly close. He states that this process is not to be overlaid on a total portfolio but can be implemented on a part of a portfolio that one can dedicate to higher risk and potentially higher rewards. And one can have a lot of fun in the process. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record